{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "210938a1",
   "metadata": {},
   "source": [
    "# Survival analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ef31aaeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from os.path import basename, exists\n",
    "\n",
    "\n",
    "def download(url):\n",
    "    filename = basename(url)\n",
    "    if not exists(filename):\n",
    "        from urllib.request import urlretrieve\n",
    "\n",
    "        local, _ = urlretrieve(url, filename)\n",
    "        print(\"Downloaded \" + local)\n",
    "\n",
    "\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/thinkstats2.py\")\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/thinkplot.py\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "babb4f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import thinkstats2\n",
    "import thinkplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "557c52e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b04b823",
   "metadata": {},
   "source": [
    "**Survival analysis** is a way to describe how long things last. It is\n",
    "often used to study human lifetimes, but it also applies to \"survival\"\n",
    "of mechanical and electronic components, or more generally to intervals\n",
    "in time before an event.\n",
    "\n",
    "If someone you know has been diagnosed with a life-threatening disease,\n",
    "you might have seen a \"5-year survival rate,\" which is the probability\n",
    "of surviving five years after diagnosis. That estimate and related\n",
    "statistics are the result of survival analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d47eaa6",
   "metadata": {},
   "source": [
    "## Survival curves\n",
    "\n",
    "The fundamental concept in survival analysis is the **survival curve**,\n",
    "$S(t)$, which is a function that maps from a duration, $t$, to the\n",
    "probability of surviving longer than $t$. If you know the distribution\n",
    "of durations, or \"lifetimes\", finding the survival curve is easy; it's\n",
    "just the complement of the CDF: $$S(t) = 1 - CDF(t)$$ where $CDF(t)$ is\n",
    "the probability of a lifetime less than or equal to $t$.\n",
    "\n",
    "For example, in the NSFG dataset, we know the duration of 11189 complete\n",
    "pregnancies. We can read this data and compute the CDF:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c26c557c",
   "metadata": {},
   "outputs": [],
   "source": [
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/nsfg.py\")\n",
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/2002FemPreg.dct\")\n",
    "download(\n",
    "    \"https://github.com/AllenDowney/ThinkStats2/raw/master/code/2002FemPreg.dat.gz\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0104ae90",
   "metadata": {},
   "outputs": [],
   "source": [
    "download(\"https://github.com/AllenDowney/ThinkStats2/raw/master/code/survival.py\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3ae9e0a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nsfg\n",
    "\n",
    "preg = nsfg.ReadFemPreg()\n",
    "complete = preg.query('outcome in [1, 3, 4]').prglngth\n",
    "cdf = thinkstats2.Cdf(complete, label='cdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8728bbaa",
   "metadata": {},
   "source": [
    "The outcome codes `1, 3, 4` indicate live birth, stillbirth, and\n",
    "miscarriage. For this analysis I am excluding induced abortions, ectopic\n",
    "pregnancies, and pregnancies that were in progress when the respondent\n",
    "was interviewed.\n",
    "\n",
    "The DataFrame method `query` takes a boolean expression and evaluates it\n",
    "for each row, selecting the rows that yield True."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ea49cf6",
   "metadata": {},
   "source": [
    "Figure [\\[survival1\\]](#survival1){reference-type=\"ref\"\n",
    "reference=\"survival1\"} (top) shows the CDF of pregnancy length and its\n",
    "complement, the survival curve. To represent the survival curve, I\n",
    "define an object that wraps a Cdf and adapts the interface:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "6e232b5a",
   "metadata": {},
   "source": [
    "class SurvivalFunction(object):\n",
    "    def __init__(self, cdf, label=''):\n",
    "        self.cdf = cdf\n",
    "        self.label = label or cdf.label\n",
    "\n",
    "    @property\n",
    "    def ts(self):\n",
    "        return self.cdf.xs\n",
    "\n",
    "    @property\n",
    "    def ss(self):\n",
    "        return 1 - self.cdf.ps\n",
    "    \n",
    "    def __getitem__(self, t):\n",
    "        return self.Prob(t)\n",
    "\n",
    "    def Prob(self, t):\n",
    "        return 1 - self.cdf.Prob(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f138bf4d",
   "metadata": {},
   "source": [
    "`SurvivalFunction` provides two properties: `ts`, which is the sequence\n",
    "of lifetimes, and `ss`, which is the survival curve. In Python, a\n",
    "\"property\" is a method that can be invoked as if it were a variable.\n",
    "\n",
    "We can instantiate a `SurvivalFunction` by passing the CDF of lifetimes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "198a5780",
   "metadata": {},
   "outputs": [],
   "source": [
    "import survival\n",
    "\n",
    "\n",
    "def MakeSurvivalFromCdf(cdf, label=\"\"):\n",
    "    \"\"\"Makes a survival function based on a CDF.\n",
    "\n",
    "    cdf: Cdf\n",
    "\n",
    "    returns: SurvivalFunction\n",
    "    \"\"\"\n",
    "    ts = cdf.xs\n",
    "    ss = 1 - cdf.ps\n",
    "    return survival.SurvivalFunction(ts, ss, label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa85496a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sf = MakeSurvivalFromCdf(cdf, label=\"survival\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b285c880",
   "metadata": {},
   "source": [
    "`SurvivalFunction` also provides `__getitem__` and `Prob`, which\n",
    "evaluates the survival curve:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778a2b7e",
   "metadata": {},
   "source": [
    "For example, `sf[13]` is the fraction of pregnancies that proceed past\n",
    "the first trimester:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e200b9be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8602198587898829"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sf[13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9716109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1397801412101171"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdf[13]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc71b04",
   "metadata": {},
   "source": [
    "About 86% of pregnancies proceed past the first trimester; about 14% do\n",
    "not.\n",
    "\n",
    "`SurvivalFunction` provides `Render`, so we can plot `sf` using the\n",
    "functions in `thinkplot`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19b0c659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "thinkplot.Plot(sf)\n",
    "thinkplot.Cdf(cdf, alpha=0.2)\n",
    "thinkplot.Config(loc=\"center left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bd21a16",
   "metadata": {},
   "source": [
    "Figure [\\[survival1\\]](#survival1){reference-type=\"ref\"\n",
    "reference=\"survival1\"} (top) shows the result. The curve is nearly flat\n",
    "between 13 and 26 weeks, which shows that few pregnancies end in the\n",
    "second trimester. And the curve is steepest around 39 weeks, which is\n",
    "the most common pregnancy length."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28a794f9",
   "metadata": {},
   "source": [
    "## Hazard function\n",
    "\n",
    "From the survival curve we can derive the **hazard function**; for\n",
    "pregnancy lengths, the hazard function maps from a time, $t$, to the\n",
    "fraction of pregnancies that continue until $t$ and then end at $t$. To\n",
    "be more precise: $$\\lambda(t) = \\frac{S(t) - S(t+1)}{S(t)}$$ The\n",
    "numerator is the fraction of lifetimes that end at $t$, which is also\n",
    "$\\PMF(t)$.\n",
    "\n",
    "`SurvivalFunction` provides `MakeHazard`, which calculates the hazard\n",
    "function:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "e3794cc3",
   "metadata": {},
   "source": [
    "# class SurvivalFunction\n",
    "\n",
    "    def MakeHazard(self, label=''):\n",
    "        ss = self.ss\n",
    "        lams = {}\n",
    "        for i, t in enumerate(self.ts[:-1]):\n",
    "            hazard = (ss[i] - ss[i+1]) / ss[i]\n",
    "            lams[t] = hazard\n",
    "\n",
    "        return HazardFunction(lams, label=label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "207a5f48",
   "metadata": {},
   "source": [
    "The `HazardFunction` object is a wrapper for a pandas Series:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3dcfba19",
   "metadata": {},
   "source": [
    "class HazardFunction(object):\n",
    "\n",
    "    def __init__(self, d, label=''):\n",
    "        self.series = pd.Series(d)\n",
    "        self.label = label"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b378da2",
   "metadata": {},
   "source": [
    "`d` can be a dictionary or any other type that can initialize a Series,\n",
    "including another Series. `label` is a string used to identify the\n",
    "HazardFunction when plotted.\n",
    "\n",
    "`HazardFunction` provides `__getitem__`, so we can evaluate it like\n",
    "this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e79b4dea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6767068273092369"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hf = sf.MakeHazardFunction(label=\"hazard\")\n",
    "hf[39]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ff26d3d",
   "metadata": {},
   "source": [
    "So of all pregnancies that proceed until week 39, about 50% end in week\n",
    "39.\n",
    "\n",
    "Figure [\\[survival1\\]](#survival1){reference-type=\"ref\"\n",
    "reference=\"survival1\"} (bottom) shows the hazard function for pregnancy\n",
    "lengths. For times after week 42, the hazard function is erratic because\n",
    "it is based on a small number of cases. Other than that the shape of the\n",
    "curve is as expected: it is highest around 39 weeks, and a little higher\n",
    "in the first trimester than in the second.\n",
    "\n",
    "The hazard function is useful in its own right, but it is also an\n",
    "important tool for estimating survival curves, as we'll see in the next\n",
    "section."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e76885a6",
   "metadata": {},
   "source": [
    "## Inferring survival curves\n",
    "\n",
    "If someone gives you the CDF of lifetimes, it is easy to compute the\n",
    "survival and hazard functions. But in many real-world scenarios, we\n",
    "can't measure the distribution of lifetimes directly. We have to infer\n",
    "it.\n",
    "\n",
    "For example, suppose you are following a group of patients to see how\n",
    "long they survive after diagnosis. Not all patients are diagnosed on the\n",
    "same day, so at any point in time, some patients have survived longer\n",
    "than others. If some patients have died, we know their survival times.\n",
    "For patients who are still alive, we don't know survival times, but we\n",
    "have a lower bound.\n",
    "\n",
    "If we wait until all patients are dead, we can compute the survival\n",
    "curve, but if we are evaluating the effectiveness of a new treatment, we\n",
    "can't wait that long! We need a way to estimate survival curves using\n",
    "incomplete information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116f50ba",
   "metadata": {},
   "source": [
    "As a more cheerful example, I will use NSFG data to quantify how long\n",
    "respondents \"survive\" until they get married for the first time. The\n",
    "range of respondents' ages is 14 to 44 years, so the dataset provides a\n",
    "snapshot of women at different stages in their lives.\n",
    "\n",
    "For women who have been married, the dataset includes the date of their\n",
    "first marriage and their age at the time. For women who have not been\n",
    "married, we know their age when interviewed, but have no way of knowing\n",
    "when or if they will get married.\n",
    "\n",
    "Since we know the age at first marriage for *some* women, it might be\n",
    "tempting to exclude the rest and compute the CDF of the known data. That\n",
    "is a bad idea. The result would be doubly misleading: (1) older women\n",
    "would be overrepresented, because they are more likely to be married\n",
    "when interviewed, and (2) married women would be overrepresented! In\n",
    "fact, this analysis would lead to the conclusion that all women get\n",
    "married, which is obviously incorrect."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c458c8d",
   "metadata": {},
   "source": [
    "## Kaplan-Meier estimation\n",
    "\n",
    "In this example it is not only desirable but necessary to include\n",
    "observations of unmarried women, which brings us to one of the central\n",
    "algorithms in survival analysis, **Kaplan-Meier estimation**.\n",
    "\n",
    "The general idea is that we can use the data to estimate the hazard\n",
    "function, then convert the hazard function to a survival curve. To\n",
    "estimate the hazard function, we consider, for each age, (1) the number\n",
    "of women who got married at that age and (2) the number of women \"at\n",
    "risk\" of getting married, which includes all women who were not married\n",
    "at an earlier age.\n",
    "\n",
    "Here's the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5d357255",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from survival import HazardFunction\n",
    "\n",
    "\n",
    "def EstimateHazardFunction(complete, ongoing, label=\"\", verbose=False):\n",
    "    \"\"\"Estimates the hazard function by Kaplan-Meier.\n",
    "\n",
    "    http://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator\n",
    "\n",
    "    complete: list of complete lifetimes\n",
    "    ongoing: list of ongoing lifetimes\n",
    "    label: string\n",
    "    verbose: whether to display intermediate results\n",
    "    \"\"\"\n",
    "    if np.sum(np.isnan(complete)):\n",
    "        raise ValueError(\"complete contains NaNs\")\n",
    "    if np.sum(np.isnan(ongoing)):\n",
    "        raise ValueError(\"ongoing contains NaNs\")\n",
    "\n",
    "    hist_complete = Counter(complete)\n",
    "    hist_ongoing = Counter(ongoing)\n",
    "\n",
    "    ts = list(hist_complete | hist_ongoing)\n",
    "    ts.sort()\n",
    "\n",
    "    at_risk = len(complete) + len(ongoing)\n",
    "\n",
    "    lams = pd.Series(index=ts, dtype=float)\n",
    "    for t in ts:\n",
    "        ended = hist_complete[t]\n",
    "        censored = hist_ongoing[t]\n",
    "\n",
    "        lams[t] = ended / at_risk\n",
    "        if verbose:\n",
    "            print(t, at_risk, ended, censored, lams[t])\n",
    "        at_risk -= ended + censored\n",
    "\n",
    "    return survival.HazardFunction(lams, label=label)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73405755",
   "metadata": {},
   "source": [
    "`complete` is the set of complete observations; in this case, the ages\n",
    "when respondents got married. `ongoing` is the set of incomplete\n",
    "observations; that is, the ages of unmarried women when they were\n",
    "interviewed.\n",
    "\n",
    "First, we precompute `hist_complete`, which is a Counter that maps from\n",
    "each age to the number of women married at that age, and `hist_ongoing`\n",
    "which maps from each age to the number of unmarried women interviewed at\n",
    "that age.\n",
    "\n",
    "`ts` is the union of ages when respondents got married and ages when\n",
    "unmarried women were interviewed, sorted in increasing order.\n",
    "\n",
    "`at_risk` keeps track of the number of respondents considered \"at risk\"\n",
    "at each age; initially, it is the total number of respondents.\n",
    "\n",
    "The result is stored in a Pandas `Series` that maps from each age to the\n",
    "estimated hazard function at that age."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "928bd259",
   "metadata": {},
   "source": [
    "Each time through the loop, we consider one age, `t`, and compute the\n",
    "number of events that end at `t` (that is, the number of respondents\n",
    "married at that age) and the number of events censored at `t` (that is,\n",
    "the number of women interviewed at `t` whose future marriage dates are\n",
    "censored). In this context, \"censored\" means that the data are\n",
    "unavailable because of the data collection process.\n",
    "\n",
    "The estimated hazard function is the fraction of the cases at risk that\n",
    "end at `t`.\n",
    "\n",
    "At the end of the loop, we subtract from `at_risk` the number of cases\n",
    "that ended or were censored at `t`.\n",
    "\n",
    "Finally, we pass `lams` to the `HazardFunction` constructor and return\n",
    "the result."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f625fb00",
   "metadata": {},
   "source": [
    "## The marriage curve\n",
    "\n",
    "To test this function, we have to do some data cleaning and\n",
    "transformation. The NSFG variables we need are:\n",
    "\n",
    "-   `cmbirth`: The respondent's date of birth, known for all\n",
    "    respondents.\n",
    "\n",
    "-   `cmintvw`: The date the respondent was interviewed, known for all\n",
    "    respondents.\n",
    "\n",
    "-   `cmmarrhx`: The date the respondent was first married, if applicable\n",
    "    and known.\n",
    "\n",
    "-   `evrmarry`: 1 if the respondent had been married prior to the date\n",
    "    of interview, 0 otherwise.\n",
    "\n",
    "The first three variables are encoded in \"century-months\"; that is, the\n",
    "integer number of months since December 1899. So century-month 1 is\n",
    "January 1900.\n",
    "\n",
    "First, we read the respondent file and replace invalid values of\n",
    "`cmmarrhx`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e3950e7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nsfg\n",
    "\n",
    "resp = nsfg.ReadFemResp()\n",
    "resp['cmmarrhx'] = resp.cmmarrhx.replace([9997, 9998, 9999], np.nan)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa2a445c",
   "metadata": {},
   "source": [
    "Then we compute each respondent's age when married and age when\n",
    "interviewed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9c98df13",
   "metadata": {},
   "outputs": [],
   "source": [
    "resp['agemarry'] = (resp.cmmarrhx - resp.cmbirth) / 12.0\n",
    "resp['age'] = (resp.cmintvw - resp.cmbirth) / 12.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a32f7e7",
   "metadata": {},
   "source": [
    "Next we extract `complete`, which is the age at marriage for women who\n",
    "have been married, and `ongoing`, which is the age at interview for\n",
    "women who have not:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d82682c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "complete = resp[resp.evrmarry==1].agemarry.dropna()\n",
    "ongoing = resp[resp.evrmarry==0].age"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc25994",
   "metadata": {},
   "source": [
    "Finally we compute the hazard function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "62d602ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hf = EstimateHazardFunction(complete, ongoing)\n",
    "thinkplot.Plot(hf)\n",
    "thinkplot.Config(xlabel=\"Age (years)\", ylabel=\"Hazard\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "642dcbc4",
   "metadata": {},
   "source": [
    "Figure [\\[survival2\\]](#survival2){reference-type=\"ref\"\n",
    "reference=\"survival2\"} (top) shows the estimated hazard function; it is\n",
    "low in the teens, higher in the 20s, and declining in the 30s. It\n",
    "increases again in the 40s, but that is an artifact of the estimation\n",
    "process; as the number of respondents \"at risk\" decreases, a small\n",
    "number of women getting married yields a large estimated hazard. The\n",
    "survival curve will smooth out this noise."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc71430f",
   "metadata": {},
   "source": [
    "## Estimating the survival curve\n",
    "\n",
    "Once we have the hazard function, we can estimate the survival curve.\n",
    "The chance of surviving past time `t` is the chance of surviving all\n",
    "times up through `t`, which is the cumulative product of the\n",
    "complementary hazard function:\n",
    "$$[1-\\lambda(0)] [1-\\lambda(1)] \\ldots [1-\\lambda(t)]$$ The\n",
    "`HazardFunction` class provides `MakeSurvival`, which computes this\n",
    "product:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "58618134",
   "metadata": {},
   "source": [
    "# class HazardFunction:\n",
    "\n",
    "    def MakeSurvival(self):\n",
    "        ts = self.series.index\n",
    "        ss = (1 - self.series).cumprod()\n",
    "        cdf = thinkstats2.Cdf(ts, 1-ss)\n",
    "        sf = SurvivalFunction(cdf)\n",
    "        return sf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09661376",
   "metadata": {},
   "source": [
    "`ts` is the sequence of times where the hazard function is estimated.\n",
    "`ss` is the cumulative product of the complementary hazard function, so\n",
    "it is the survival curve.\n",
    "\n",
    "Because of the way `SurvivalFunction` is implemented, we have to compute\n",
    "the complement of `ss`, make a Cdf, and then instantiate a\n",
    "SurvivalFunction object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "eebdb32c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sf = hf.MakeSurvival()\n",
    "thinkplot.Plot(sf)\n",
    "thinkplot.Config(xlabel=\"Age (years)\", ylabel=\"Prob unmarried\", ylim=[0, 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b69d128",
   "metadata": {},
   "source": [
    "Figure [\\[survival2\\]](#survival2){reference-type=\"ref\"\n",
    "reference=\"survival2\"} (bottom) shows the result. The survival curve is\n",
    "steepest between 25 and 35, when most women get married. Between 35 and\n",
    "45, the curve is nearly flat, indicating that women who do not marry\n",
    "before age 35 are unlikely to get married.\n",
    "\n",
    "A curve like this was the basis of a famous magazine article in 1986;\n",
    "*Newsweek* reported that a 40-year old unmarried woman was \"more likely\n",
    "to be killed by a terrorist\" than get married. These statistics were\n",
    "widely reported and became part of popular culture, but they were wrong\n",
    "then (because they were based on faulty analysis) and turned out to be\n",
    "even more wrong (because of cultural changes that were already in\n",
    "progress and continued). In 2006, *Newsweek* ran an another article\n",
    "admitting that they were wrong.\n",
    "\n",
    "I encourage you to read more about this article, the statistics it was\n",
    "based on, and the reaction. It should remind you of the ethical\n",
    "obligation to perform statistical analysis with care, interpret the\n",
    "results with appropriate skepticism, and present them to the public\n",
    "accurately and honestly."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f540f8",
   "metadata": {},
   "source": [
    "## Confidence intervals\n",
    "\n",
    "Kaplan-Meier analysis yields a single estimate of the survival curve,\n",
    "but it is also important to quantify the uncertainty of the estimate. As\n",
    "usual, there are three possible sources of error: measurement error,\n",
    "sampling error, and modeling error.\n",
    "\n",
    "In this example, measurement error is probably small. People generally\n",
    "know when they were born, whether they've been married, and when. And\n",
    "they can be expected to report this information accurately.\n",
    "\n",
    "We can quantify sampling error by resampling. Here's the code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "56f6f09b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def EstimateMarriageSurvival(resp):\n",
    "    \"\"\"Estimates the survival curve.\n",
    "\n",
    "    resp: DataFrame of respondents\n",
    "\n",
    "    returns: pair of HazardFunction, SurvivalFunction\n",
    "    \"\"\"\n",
    "    # NOTE: Filling missing values would be better than dropping them.\n",
    "    complete = resp[resp.evrmarry == 1].agemarry.dropna()\n",
    "    ongoing = resp[resp.evrmarry == 0].age\n",
    "\n",
    "    hf = EstimateHazardFunction(complete, ongoing)\n",
    "    sf = hf.MakeSurvival()\n",
    "\n",
    "    return hf, sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7dc3a578",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ResampleSurvival(resp, iters=101):\n",
    "    \"\"\"Resamples respondents and estimates the survival function.\n",
    "\n",
    "    resp: DataFrame of respondents\n",
    "    iters: number of resamples\n",
    "    \"\"\"\n",
    "    _, sf = EstimateMarriageSurvival(resp)\n",
    "    thinkplot.Plot(sf)\n",
    "\n",
    "    low, high = resp.agemarry.min(), resp.agemarry.max()\n",
    "    ts = np.arange(low, high, 1 / 12.0)\n",
    "\n",
    "    ss_seq = []\n",
    "    for _ in range(iters):\n",
    "        sample = thinkstats2.ResampleRowsWeighted(resp)\n",
    "        _, sf = EstimateMarriageSurvival(sample)\n",
    "        ss_seq.append(sf.Probs(ts))\n",
    "\n",
    "    low, high = thinkstats2.PercentileRows(ss_seq, [5, 95])\n",
    "    thinkplot.FillBetween(ts, low, high, color=\"gray\", label=\"90% CI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4ecc60c",
   "metadata": {},
   "source": [
    "`ResampleSurvival` takes `resp`, a DataFrame of respondents, and\n",
    "`iters`, the number of times to resample. It computes `ts`, which is the\n",
    "sequence of ages where we will evaluate the survival curves.\n",
    "\n",
    "Inside the loop, `ResampleSurvival`:\n",
    "\n",
    "-   Resamples the respondents using `ResampleRowsWeighted`, which we saw\n",
    "    in Section [\\[weighted\\]](#weighted){reference-type=\"ref\"\n",
    "    reference=\"weighted\"}.\n",
    "\n",
    "-   Calls `EstimateSurvival`, which uses the process in the previous\n",
    "    sections to estimate the hazard and survival curves, and\n",
    "\n",
    "-   Evaluates the survival curve at each age in `ts`.\n",
    "\n",
    "`ss_seq` is a sequence of evaluated survival curves. `PercentileRows`\n",
    "takes this sequence and computes the 5th and 95th percentiles, returning\n",
    "a 90% confidence interval for the survival curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "72c40c1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ResampleSurvival(resp)\n",
    "thinkplot.Config(\n",
    "    xlabel=\"Age (years)\",\n",
    "    ylabel=\"Prob unmarried\",\n",
    "    xlim=[12, 46],\n",
    "    ylim=[0, 1],\n",
    "    loc=\"upper right\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fadd6e6",
   "metadata": {},
   "source": [
    "Figure [\\[survival3\\]](#survival3){reference-type=\"ref\"\n",
    "reference=\"survival3\"} shows the result along with the survival curve we\n",
    "estimated in the previous section. The confidence interval takes into\n",
    "account the sampling weights, unlike the estimated curve. The\n",
    "discrepancy between them indicates that the sampling weights have a\n",
    "substantial effect on the estimate---we will have to keep that in mind."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "343cbdef",
   "metadata": {},
   "source": [
    "## Cohort effects\n",
    "\n",
    "One of the challenges of survival analysis is that different parts of\n",
    "the estimated curve are based on different groups of respondents. The\n",
    "part of the curve at time `t` is based on respondents whose age was at\n",
    "least `t` when they were interviewed. So the leftmost part of the curve\n",
    "includes data from all respondents, but the rightmost part includes only\n",
    "the oldest respondents.\n",
    "\n",
    "If the relevant characteristics of the respondents are not changing over\n",
    "time, that's fine, but in this case it seems likely that marriage\n",
    "patterns are different for women born in different generations. We can\n",
    "investigate this effect by grouping respondents according to their\n",
    "decade of birth. Groups like this, defined by date of birth or similar\n",
    "events, are called **cohorts**, and differences between the groups are\n",
    "called **cohort effects**.\n",
    "\n",
    "To investigate cohort effects in the NSFG marriage data, I gathered the\n",
    "Cycle 6 data from 2002 used throughout this book; the Cycle 7 data from\n",
    "2006--2010 used in\n",
    "Section [\\[replication\\]](#replication){reference-type=\"ref\"\n",
    "reference=\"replication\"}; and the Cycle 5 data from 1995. In total these\n",
    "datasets include 30,769 respondents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8181550b",
   "metadata": {},
   "outputs": [],
   "source": [
    "download(\n",
    "    \"https://github.com/AllenDowney/ThinkStats2/raw/master/code/1995FemRespData.dat.gz\"\n",
    ")\n",
    "download(\n",
    "    \"https://github.com/AllenDowney/ThinkStats2/raw/master/code/2006_2010_FemRespSetup.dct\"\n",
    ")\n",
    "download(\n",
    "    \"https://github.com/AllenDowney/ThinkStats2/raw/master/code/2006_2010_FemResp.dat.gz\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "baf6dc1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "resp5 = nsfg.ReadFemResp1995()\n",
    "resp6 = nsfg.ReadFemResp2002()\n",
    "resp7 = nsfg.ReadFemResp2010()\n",
    "resps = [resp5, resp6, resp7]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2a76bef",
   "metadata": {},
   "source": [
    "For each DataFrame, `resp`, I use `cmbirth` to compute the decade of\n",
    "birth for each respondent:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "99d32da6",
   "metadata": {},
   "outputs": [],
   "source": [
    "month0 = pd.to_datetime('1899-12-15')\n",
    "dates = [month0 + pd.DateOffset(months=cm) for cm in resp.cmbirth]\n",
    "resp['decade'] = (pd.DatetimeIndex(dates).year - 1900) // 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b4d57e75",
   "metadata": {},
   "outputs": [],
   "source": [
    "def AddLabelsByDecade(groups, **options):\n",
    "    \"\"\"Draws fake points in order to add labels to the legend.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    thinkplot.PrePlot(len(groups))\n",
    "    for name, _ in groups:\n",
    "        label = \"%d0s\" % name\n",
    "        thinkplot.Plot([15], [1], label=label, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "d9527759",
   "metadata": {},
   "outputs": [],
   "source": [
    "def EstimateMarriageSurvivalByDecade(groups, **options):\n",
    "    \"\"\"Groups respondents by decade and plots survival curves.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    thinkplot.PrePlot(len(groups))\n",
    "    for _, group in groups:\n",
    "        _, sf = EstimateMarriageSurvival(group)\n",
    "        thinkplot.Plot(sf, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6a4c47d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotResampledByDecade(resps, iters=11, predict_flag=False, omit=None):\n",
    "    \"\"\"Plots survival curves for resampled data.\n",
    "\n",
    "    resps: list of DataFrames\n",
    "    iters: number of resamples to plot\n",
    "    predict_flag: whether to also plot predictions\n",
    "    \"\"\"\n",
    "    for i in range(iters):\n",
    "        samples = [thinkstats2.ResampleRowsWeighted(resp) for resp in resps]\n",
    "        sample = pd.concat(samples, ignore_index=True)\n",
    "        groups = sample.groupby(\"decade\")\n",
    "\n",
    "        if omit:\n",
    "            groups = [(name, group) for name, group in groups if name not in omit]\n",
    "\n",
    "        # TODO: refactor this to collect resampled estimates and\n",
    "        # plot shaded areas\n",
    "        if i == 0:\n",
    "            AddLabelsByDecade(groups, alpha=0.7)\n",
    "\n",
    "        if predict_flag:\n",
    "            PlotPredictionsByDecade(groups, alpha=0.1)\n",
    "            EstimateMarriageSurvivalByDecade(groups, alpha=0.1)\n",
    "        else:\n",
    "            EstimateMarriageSurvivalByDecade(groups, alpha=0.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29fe0afe",
   "metadata": {},
   "source": [
    "`cmbirth` is encoded as the integer number of months since December\n",
    "1899; `month0` represents that date as a Timestamp object. For each\n",
    "birth date, we instantiate a `DateOffset` that contains the\n",
    "century-month and add it to `month0`; the result is a sequence of\n",
    "Timestamps, which is converted to a `DateTimeIndex`. Finally, we extract\n",
    "`year` and compute decades.\n",
    "\n",
    "To take into account the sampling weights, and also to show variability\n",
    "due to sampling error, I resample the data, group respondents by decade,\n",
    "and plot survival curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "09ab5200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotResampledByDecade(resps)\n",
    "thinkplot.Config(\n",
    "    xlabel=\"Age (years)\", ylabel=\"Prob unmarried\", xlim=[13, 45], ylim=[0, 1]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4943c6c3",
   "metadata": {},
   "source": [
    "Data from the three NSFG cycles use different sampling weights, so I\n",
    "resample them separately and then use `concat` to merge them into a\n",
    "single DataFrame. The parameter `ignore_index` tells `concat` not to\n",
    "match up respondents by index; instead it creates a new index from 0 to\n",
    "30768.\n",
    "\n",
    "`EstimateSurvivalByDecade` plots survival curves for each cohort:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d12c58",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "Figure [\\[survival4\\]](#survival4){reference-type=\"ref\"\n",
    "reference=\"survival4\"} shows the results. Several patterns are visible:\n",
    "\n",
    "-   Women born in the 50s married earliest, with successive cohorts\n",
    "    marrying later and later, at least until age 30 or so.\n",
    "\n",
    "-   Women born in the 60s follow a surprising pattern. Prior to age 25,\n",
    "    they were marrying at slower rates than their predecessors. After\n",
    "    age 25, they were marrying faster. By age 32 they had overtaken the\n",
    "    50s cohort, and at age 44 they are substantially more likely to have\n",
    "    married."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "159211e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "Women born in the 60s turned 25 between 1985 and 1995. Remembering\n",
    "that the *Newsweek* article I mentioned was published in 1986, it is\n",
    "tempting to imagine that the article triggered a marriage boom. That\n",
    "explanation would be too pat, but it is possible that the article\n",
    "and the reaction to it were indicative of a mood that affected the\n",
    "behavior of this cohort."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "61a802ba",
   "metadata": {},
   "source": [
    "-   The pattern of the 70s cohort is similar. They are less likely than\n",
    "    their predecessors to be married before age 25, but at age 35 they\n",
    "    have caught up with both of the previous cohorts.\n",
    "\n",
    "-   Women born in the 80s are even less likely to marry before age 25.\n",
    "    What happens after that is not clear; for more data, we have to wait\n",
    "    for the next cycle of the NSFG.\n",
    "\n",
    "In the meantime we can make some predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5b2e830",
   "metadata": {},
   "source": [
    "## Extrapolation\n",
    "\n",
    "The survival curve for the 70s cohort ends at about age 38; for the 80s\n",
    "cohort it ends at age 28, and for the 90s cohort we hardly have any data\n",
    "at all.\n",
    "\n",
    "We can extrapolate these curves by \"borrowing\" data from the previous\n",
    "cohort. HazardFunction provides a method, `Extend`, that copies the tail\n",
    "from another longer HazardFunction:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "049bf558",
   "metadata": {},
   "source": [
    "# class HazardFunction\n",
    "\n",
    "    def Extend(self, other):\n",
    "        last = self.series.index[-1]\n",
    "        more = other.series[other.series.index > last]\n",
    "        self.series = pd.concat([self.series, more])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a437f50c",
   "metadata": {},
   "source": [
    "As we saw in Section [\\[hazard\\]](#hazard){reference-type=\"ref\"\n",
    "reference=\"hazard\"}, the HazardFunction contains a Series that maps from\n",
    "$t$ to $\\lambda(t)$. `Extend` finds `last`, which is the last index in\n",
    "`self.series`, selects values from `other` that come later than `last`,\n",
    "and appends them onto `self.series`.\n",
    "\n",
    "Now we can extend the HazardFunction for each cohort, using values from\n",
    "the predecessor:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "03e8833a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def PlotPredictionsByDecade(groups, **options):\n",
    "    \"\"\"Groups respondents by decade and plots survival curves.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    hfs = []\n",
    "    for _, group in groups:\n",
    "        hf, sf = EstimateMarriageSurvival(group)\n",
    "        hfs.append(hf)\n",
    "\n",
    "    thinkplot.PrePlot(len(hfs))\n",
    "    for i, hf in enumerate(hfs):\n",
    "        if i > 0:\n",
    "            hf.Extend(hfs[i - 1])\n",
    "        sf = hf.MakeSurvival()\n",
    "        thinkplot.Plot(sf, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2b52f7d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "PlotResampledByDecade(resps, predict_flag=True)\n",
    "thinkplot.Config(\n",
    "    xlabel=\"Age (years)\", ylabel=\"Prob unmarried\", xlim=[13, 45], ylim=[0, 1]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "585d7f6b",
   "metadata": {},
   "source": [
    "`groups` is a GroupBy object with respondents grouped by decade of\n",
    "birth. The first loop computes the HazardFunction for each group.\n",
    "\n",
    "The second loop extends each HazardFunction with values from its\n",
    "predecessor, which might contain values from the previous group, and so\n",
    "on. Then it converts each HazardFunction to a SurvivalFunction and plots\n",
    "it."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "544a405d",
   "metadata": {},
   "source": [
    "Figure [\\[survival5\\]](#survival5){reference-type=\"ref\"\n",
    "reference=\"survival5\"} shows the results; I've removed the 50s cohort to\n",
    "make the predictions more visible. These results suggest that by age 40,\n",
    "the most recent cohorts will converge with the 60s cohort, with fewer\n",
    "than 20% never married."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8516df63",
   "metadata": {},
   "source": [
    "## Expected remaining lifetime\n",
    "\n",
    "Given a survival curve, we can compute the expected remaining lifetime\n",
    "as a function of current age. For example, given the survival curve of\n",
    "pregnancy length from\n",
    "Section [\\[survival\\]](#survival){reference-type=\"ref\"\n",
    "reference=\"survival\"}, we can compute the expected time until delivery.\n",
    "\n",
    "The first step is to extract the PMF of lifetimes. `SurvivalFunction`\n",
    "provides a method that does that:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "2da7a5b3",
   "metadata": {},
   "source": [
    "# class SurvivalFunction\n",
    "\n",
    "    def MakePmf(self, filler=None):\n",
    "        pmf = thinkstats2.Pmf()\n",
    "        for val, prob in self.cdf.Items():\n",
    "            pmf.Set(val, prob)\n",
    "\n",
    "        cutoff = self.cdf.ps[-1]\n",
    "        if filler is not None:\n",
    "            pmf[filler] = 1-cutoff\n",
    "\n",
    "        return pmf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff0798af",
   "metadata": {},
   "source": [
    "Remember that the SurvivalFunction contains the Cdf of lifetimes. The\n",
    "loop copies the values and probabilities from the Cdf into a Pmf.\n",
    "\n",
    "`cutoff` is the highest probability in the Cdf, which is 1 if the Cdf is\n",
    "complete, and otherwise less than 1. If the Cdf is incomplete, we plug\n",
    "in the provided value, `filler`, to cap it off.\n",
    "\n",
    "The Cdf of pregnancy lengths is complete, so we don't have to worry\n",
    "about this detail yet.\n",
    "\n",
    "The next step is to compute the expected remaining lifetime, where\n",
    "\"expected\" means average. `SurvivalFunction` provides a method that does\n",
    "that, too:"
   ]
  },
  {
   "cell_type": "raw",
   "id": "cb95f045",
   "metadata": {},
   "source": [
    "# class SurvivalFunction\n",
    "\n",
    "    def RemainingLifetime(self, filler=None, func=thinkstats2.Pmf.Mean):\n",
    "        pmf = self.MakePmf(filler=filler)\n",
    "        d = {}\n",
    "        for t in sorted(pmf.Values())[:-1]:\n",
    "            pmf[t] = 0\n",
    "            pmf.Normalize()\n",
    "            d[t] = func(pmf) - t\n",
    "\n",
    "        return pd.Series(d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f673e5e2",
   "metadata": {},
   "source": [
    "`RemainingLifetime` takes `filler`, which is passed along to `MakePmf`,\n",
    "and `func` which is the function used to summarize the distribution of\n",
    "remaining lifetimes.\n",
    "\n",
    "`pmf` is the Pmf of lifetimes extracted from the SurvivalFunction. `d`\n",
    "is a dictionary that contains the results, a map from current age, `t`,\n",
    "to expected remaining lifetime.\n",
    "\n",
    "The loop iterates through the values in the Pmf. For each value of `t`\n",
    "it computes the conditional distribution of lifetimes, given that the\n",
    "lifetime exceeds `t`. It does that by removing values from the Pmf one\n",
    "at a time and renormalizing the remaining values.\n",
    "\n",
    "Then it uses `func` to summarize the conditional distribution. In this\n",
    "example the result is the mean pregnancy length, given that the length\n",
    "exceeds `t`. By subtracting `t` we get the mean remaining pregnancy\n",
    "length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c5cfed1d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of complete pregnancies 11189\n",
      "Number of ongoing pregnancies 352\n"
     ]
    }
   ],
   "source": [
    "preg = nsfg.ReadFemPreg()\n",
    "\n",
    "complete = preg.query(\"outcome in [1, 3, 4]\").prglngth\n",
    "print(\"Number of complete pregnancies\", len(complete))\n",
    "ongoing = preg[preg.outcome == 6].prglngth\n",
    "print(\"Number of ongoing pregnancies\", len(ongoing))\n",
    "\n",
    "hf = EstimateHazardFunction(complete, ongoing)\n",
    "sf1 = hf.MakeSurvival()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dc9dabdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rem_life1 = sf1.RemainingLifetime()\n",
    "thinkplot.Plot(rem_life1)\n",
    "thinkplot.Config(\n",
    "    title=\"Remaining pregnancy length\", xlabel=\"Weeks\", ylabel=\"Mean remaining weeks\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ff9bb9",
   "metadata": {},
   "source": [
    "Figure [\\[survival6\\]](#survival6){reference-type=\"ref\"\n",
    "reference=\"survival6\"} (left) shows the expected remaining pregnancy\n",
    "length as a function of the current duration. For example, during Week\n",
    "0, the expected remaining duration is about 34 weeks. That's less than\n",
    "full term (39 weeks) because terminations of pregnancy in the first\n",
    "trimester bring the average down.\n",
    "\n",
    "The curve drops slowly during the first trimester. After 13 weeks, the\n",
    "expected remaining lifetime has dropped by only 9 weeks, to 25. After\n",
    "that the curve drops faster, by about a week per week.\n",
    "\n",
    "Between Week 37 and 42, the curve levels off between 1 and 2 weeks. At\n",
    "any time during this period, the expected remaining lifetime is the\n",
    "same; with each week that passes, the destination gets no closer.\n",
    "Processes with this property are called **memoryless** because the past\n",
    "has no effect on the predictions. This behavior is the mathematical\n",
    "basis of the infuriating mantra of obstetrics nurses: \"any day now.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c066c2df",
   "metadata": {},
   "source": [
    "Figure [\\[survival6\\]](#survival6){reference-type=\"ref\"\n",
    "reference=\"survival6\"} (right) shows the median remaining time until\n",
    "first marriage, as a function of age. For an 11 year-old girl, the\n",
    "median time until first marriage is about 14 years. The curve decreases\n",
    "until age 22 when the median remaining time is about 7 years. After that\n",
    "it increases again: by age 30 it is back where it started, at 14 years.\n",
    "\n",
    "Based on this data, young women have decreasing remaining \"lifetimes\".\n",
    "Mechanical components with this property are called **NBUE** for \"new\n",
    "better than used in expectation,\" meaning that a new part is expected to\n",
    "last longer.\n",
    "\n",
    "Women older than 22 have increasing remaining time until first marriage.\n",
    "Components with this property are called **UBNE** for \"used better than\n",
    "new in expectation.\" That is, the older the part, the longer it is\n",
    "expected to last. Newborns and cancer patients are also UBNE; their life\n",
    "expectancy increases the longer they live."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb9ce15",
   "metadata": {},
   "source": [
    "For this example I computed median, rather than mean, because the Cdf is\n",
    "incomplete; the survival curve projects that about 20% of respondents\n",
    "will not marry before age 44. The age of first marriage for these women\n",
    "is unknown, and might be non-existent, so we can't compute a mean.\n",
    "\n",
    "I deal with these unknown values by replacing them with `np.inf`, a\n",
    "special value that represents infinity. That makes the mean infinity for\n",
    "all ages, but the median is well-defined as long as more than 50% of the\n",
    "remaining lifetimes are finite, which is true until age 30. After that\n",
    "it is hard to define a meaningful expected remaining lifetime.\n",
    "\n",
    "Here's the code that computes and plots these functions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bedfc062",
   "metadata": {},
   "outputs": [],
   "source": [
    "hf, sf2 = EstimateMarriageSurvival(resp6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2f1453fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "func = lambda pmf: pmf.Percentile(50)\n",
    "rem_life2 = sf2.RemainingLifetime(filler=np.inf, func=func)\n",
    "\n",
    "thinkplot.Plot(rem_life2)\n",
    "thinkplot.Config(\n",
    "    title=\"Years until first marriage\",\n",
    "    ylim=[0, 15],\n",
    "    xlim=[11, 31],\n",
    "    xlabel=\"Age (years)\",\n",
    "    ylabel=\"Median remaining years\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b235c331",
   "metadata": {},
   "source": [
    "`sf1` is the survival curve for pregnancy length; in this case we can\n",
    "use the default values for `RemainingLifetime`.\n",
    "\n",
    "`sf2` is the survival curve for age at first marriage; `func` is a\n",
    "function that takes a Pmf and computes its median (50th percentile)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "36c47cb8",
   "metadata": {},
   "source": [
    "## Glossary\n",
    "\n",
    "-   **survival analysis**: A set of methods for describing and\n",
    "    predicting lifetimes, or more generally time until an event occurs.\n",
    "\n",
    "-   **survival curve**: A function that maps from a time, $t$, to the\n",
    "    probability of surviving past $t$.\n",
    "\n",
    "-   **hazard function**: A function that maps from $t$ to the fraction\n",
    "    of people alive until $t$ who die at $t$.\n",
    "\n",
    "-   **Kaplan-Meier estimation**: An algorithm for estimating hazard and\n",
    "    survival functions.\n",
    "\n",
    "-   **cohort**: a group of subjects defined by an event, like date of\n",
    "    birth, in a particular interval of time.\n",
    "\n",
    "-   **cohort effect**: a difference between cohorts.\n",
    "\n",
    "-   **NBUE**: A property of expected remaining lifetime, \"New better\n",
    "    than used in expectation.\"\n",
    "\n",
    "-   **UBNE**: A property of expected remaining lifetime, \"Used better\n",
    "    than new in expectation.\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2976a19b",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1c1fe48",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Exercise:**    In NSFG Cycles 6 and 7, the variable `cmdivorcx` contains the date of divorce for the respondent’s first marriage, if applicable, encoded in century-months.\n",
    "\n",
    "Compute the duration of marriages that have ended in divorce, and the duration, so far, of marriages that are ongoing. Estimate the hazard and survival curve for the duration of marriage.\n",
    "\n",
    "Use resampling to take into account sampling weights, and plot data from several resamples to visualize sampling error.\n",
    "\n",
    "Consider dividing the respondents into groups by decade of birth, and possibly by age at first marriage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2391998e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CleanData(resp):\n",
    "    \"\"\"Cleans respondent data.\n",
    "\n",
    "    resp: DataFrame\n",
    "    \"\"\"\n",
    "    resp[\"cmdivorcx\"] = resp.cmdivorcx.replace([9998, 9999], np.nan)\n",
    "    resp[\"notdivorced\"] = resp.cmdivorcx.isnull().astype(int)\n",
    "    resp[\"duration\"] = (resp.cmdivorcx - resp.cmmarrhx) / 12.0\n",
    "    resp[\"durationsofar\"] = (resp.cmintvw - resp.cmmarrhx) / 12.0\n",
    "\n",
    "    month0 = pd.to_datetime(\"1899-12-15\")\n",
    "    dates = [month0 + pd.DateOffset(months=cm) for cm in resp.cmbirth]\n",
    "    resp[\"decade\"] = (pd.DatetimeIndex(dates).year - 1900) // 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4812f4ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "CleanData(resp6)\n",
    "married6 = resp6[resp6.evrmarry == 1]\n",
    "\n",
    "CleanData(resp7)\n",
    "married7 = resp7[resp7.evrmarry == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0c03c1f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "\n",
    "def ResampleDivorceCurve(resps):\n",
    "    \"\"\"Plots divorce curves based on resampled data.\n",
    "\n",
    "    resps: list of respondent DataFrames\n",
    "    \"\"\"\n",
    "    for _ in range(11):\n",
    "        samples = [thinkstats2.ResampleRowsWeighted(resp) for resp in resps]\n",
    "        sample = pd.concat(samples, ignore_index=True)\n",
    "        PlotDivorceCurveByDecade(sample, color=\"#225EA8\", alpha=0.1)\n",
    "\n",
    "    thinkplot.Show(xlabel=\"years\", axis=[0, 28, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e878a5e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "\n",
    "def ResampleDivorceCurveByDecade(resps):\n",
    "    \"\"\"Plots divorce curves for each birth cohort.\n",
    "\n",
    "    resps: list of respondent DataFrames\n",
    "    \"\"\"\n",
    "    for i in range(41):\n",
    "        samples = [thinkstats2.ResampleRowsWeighted(resp) for resp in resps]\n",
    "        sample = pd.concat(samples, ignore_index=True)\n",
    "        groups = sample.groupby(\"decade\")\n",
    "        if i == 0:\n",
    "            survival.AddLabelsByDecade(groups, alpha=0.7)\n",
    "\n",
    "        EstimateSurvivalByDecade(groups, alpha=0.1)\n",
    "\n",
    "    thinkplot.Config(xlabel=\"Years\", ylabel=\"Fraction undivorced\", axis=[0, 28, 0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c3c0fcd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "\n",
    "def EstimateSurvivalByDecade(groups, **options):\n",
    "    \"\"\"Groups respondents by decade and plots survival curves.\n",
    "\n",
    "    groups: GroupBy object\n",
    "    \"\"\"\n",
    "    thinkplot.PrePlot(len(groups))\n",
    "    for name, group in groups:\n",
    "        _, sf = EstimateSurvival(group)\n",
    "        thinkplot.Plot(sf, **options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a1470df4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Solution\n",
    "\n",
    "\n",
    "def EstimateSurvival(resp):\n",
    "    \"\"\"Estimates the survival curve.\n",
    "\n",
    "    resp: DataFrame of respondents\n",
    "\n",
    "    returns: pair of HazardFunction, SurvivalFunction\n",
    "    \"\"\"\n",
    "    complete = resp[resp.notdivorced == 0].duration.dropna()\n",
    "    ongoing = resp[resp.notdivorced == 1].durationsofar.dropna()\n",
    "\n",
    "    hf = survival.EstimateHazardFunction(complete, ongoing)\n",
    "    sf = hf.MakeSurvival()\n",
    "\n",
    "    return hf, sf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8ba1f3b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Solution\n",
    "\n",
    "ResampleDivorceCurveByDecade([married6, married7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a59ce5d0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
